Merton Models: Mapping Default of Government Bank in Indonesia

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1 Proceedings Book of 2 nd ICEFMO, 24, Malaysia Handbook on Economics, Finance and Management Outlooks ISBN: Meon Models: Mapping Default of Government Bank in Indonesia gus Munandar Faculty of Economics and Business, University of 7 ugust 945 Jakaa, Indonesia BSRC he objective of Basel II is to strengthen the financial system security by emphasizing on riskbased calculation of capital. Driven by Basel II, this paper investigates the probability of default of government banks in Indonesia in the period of 22 2 using annually-published repo of central bank (Bank Indonesia with 4 government banks as the sample of research. he probability of default is measured by using meon model. he findings that measurement results using meon approach are empirically confirmed. In 24, P Bank Rakyat Indonesia bk which gets the lowest probability of default was announced as he Best National Bank in Indonesia by Bisnis Indonesia award (Moeljono, 26. In 28, the government banks have high probability default because financial crisis of 28 (probability default more than 3%. Keywords: Probability default, Government banks, Meon model. Research Background Bank Indonesia (2 claims that the financial crisis in Indonesia induced by the fall of the exchange rate of rupiah. s a result, many banks suffered losses, especially banks that have loans in foreign currency and does not hedge the loan. he volatility of the exchange rate becomes more severe when banks have a poor of cash flow (difficulties of liquidity and solvency. Moreover, the case becomes more complex when there are mass withdrawals by customers. How to solve banking financial problems has always been an actual discussion between economists. he new Basel Capital ccord (Basel Committee on Banking Supervision, 26, in Bastos, 29 recommends that the banking sector have to estimates their financial failure (default. In Indonesia, Bank Indonesia has legislate some regulations to achieve effectiveness of the system of banking supervision in accordance with the 25 Basel Core. he topics of estimation the financial failure has become an attention-grabbing research for some decades. Manurung (29 states that the measurement of the risk of default (default probability was staed by Beaver (966 using univariate models, then ltman (968 using discriminant model (ltman's Z-Score Model. In 974, Meon modified Black-Scholes model. he last model that modified by VMR meon (Kealhofer, McQuown and Vasicek is known as the KMV model. udela and Young (23 have conducted research using meon model for companies in the UK. Hadad et al (24 have used the model to examine the companies in Indonesia. However, no studies that using meon model to estimate default probability and plot the risk of failure in banking sector in Indonesia. he findings are impoant and valuable in policy making. 5

2 2. Literature Review and Hypothesis Development Studies regarding the estimation of bankruptcy become an attention-grabbing scientists and economists for decades. Manurung (29 states that the measurement of the risk of default (default probability was staed by Beaver (966 that using univariate models. In 968, ltman using discriminant model (ltman's Z-Score Model to predict financial default. In 973, Meon published the results of his research entitled "On the Pricing of Corporate Debt: he Risk Structure of Interest Pates" which aims to analyze the failure of the company. He defines the risk of failure is the probability that the firm will be unable to satisfy some or all of the indenture require merits. he study followed by Moody Meon-KMV (23 with the title "Modeling Default R-Modeling Methodology". Moody-KMV (23 says that the Black-Scholes (973 states that the market value of the underlying assets of the company follows the stochastic process. Research SM Lin, et al, entitled "Meon or credit scoring models: modeling the default of a small business" formulate the model as follows E = max [ - X,] Where E= equity, and = sset, while X = Book value of Debt r E N d X N( Where, ( e d2 d ( e X.5 d 2 d ; d2 is the volatility (deviation of the asset value, and r is the risk-free rate of interest, both of which are assumed to be constant. herefore, N(. is the accumulation density function of the standard normal distribution. fter that, L=X * / be a measure of leverage where X * = Xe, therefore, r E N( d Xe N( d2 where, ( e d X.5 d 2 d ; Jones, Mason and Rosenfeld (984 in S-M Lin, et al stated that, E ( d E E and N E N( d LN( d2 Finally, probability of default is formulated as follow, ( L P = N(-d 2 where d2 =,5 3. Research Method 3.. Population and Sample he population is all banks in Indonesia. he sample is state banks because these banks suppoed by government. hese bank are assumed have sufficient financial resilience. Finally, the samples are Bank Negara Indonesia, Bank Rakyat Indonesia, Bank abungan Negara, and Bank Mandiri. he period of this study is Data and Data Sources he data is secondary data. Financial data downloaded from the publications of Bank Indonesia. Volatility of asset measured using indek harga saham gabungan (IHSG. he proxy of risk-free rate is is SBI rate Research Variables he dependent variable in this study is the probability of financial failure of the banking sector. he financial failure in this research is condition which the firm unable to fulfill obligations to third paies. his variable is measured as follows 6

3 ( L P = N(-d 2 where d2 =, Data Processing Meon models used in this study is the result of the development that used by SM Lin, et al. He formulated that, E = max [ - X, ] Where E = equity, and = ssets, then X = Book value of Debt where, ( e d X.5 d 2 d ; d2 is the volatility (deviation of the asset value, and r is the risk-free rate of interest, both of which are assumed to be constant. herefore, N(. is the accumulation density function of the standard normal distribution. fter that, L=X * / be a measure of leverage where X * = Xe, therefore, E r N d X N( ( e d2 where, ( e d X.5 Finally, the probability of default is formulated as follows, ( L P = N (-d 2 where d2 =,5 he data processing using excel software with the following formula, = LN ( / (X * EXP (rf * / (SIGM * SQR ( +.5 * SIGM * SQR ( Fuhermore, d 2 d computed using this formula, = LN (V / (K * EXP (rf * / (SIGM * SQR ( - SIGM * SQR ( hen, Normal distribution N (d or delta obtained with excel formulas, = NORMSDIS (d, Normal distribution N (d2 or N (d2 calculated using excel formula = NORMSDIS (d2 Fuhermore, excel formula for the default of probability as follows, = -ProbaNonBankruptcy 4. Data nalysis and Discussion Result of calculation using meon analyzed descriptively to explain probability of failure of state bank in Indonesia. he probability of default of Bank Negara Indonesia is graphically presented below, Graphic-.he probability of default of Bank Negara Indonesia 7

4 he graphic describes the fluctuations of probability of failure of Bank Negara Indonesia. Probability of failure in 28 is 33.5%. his caused by global financial crisis in 28 that affected the Indonesian banking sector. he probability of default increases since 25 (7.% because increase in world oil prices (riwibowo, et al, 28. Beside that, the failure probability that staed in 25 is also influenced by the volatility of IHSG that significantly increase after 25 as the graph below, Graphic-2. he probability of default of Bank Negara Indonesia Based on calculation using meon model, he probability of default of Bank Rakyat Indonesia is graphically presented below, Graphic-3. he probability of default of Bank Rakyat Indonesia he explanation of increasing of probability default staed from 25 is caused by increase oil price, volatility of IHSG, and global crisis. In 28, Bank Rakyat Indonesia also affected by global crisis but the probability of default is better than others state bank (33%. In 24, the probability of default is also better than others state bank (7, 4. herefore, In 24, P Bank Rakyat Indonesia bk that gets the lowest probability of default was announced as he Best National Bank in Indonesia by Bisnis Indonesia award (Moeljono, 26. In 28, the government banks have high probability default because financial crisis of 28 (probability default more than 3%. Graphic-3. he probability of default of Bank abungan Indonesia s graphic 4 below, he probability of default is also increases since 25 (9.4%. he causes of increase is explained before. In 28, Bank abungan Negara also affected by global crisis so the probability of default is 33,8%. 8

5 he last explanation is probability of default of Bank Mandiri. In 28, this bank also affected by global crisis so the probability of default is 33,7%. he graphic of probability of default of Bank Mandiri below, Graphic 4: he probability of default of Bank Mandiri 5. Conclusion Based on research data and results of the analysis can be deduced: First, the probability of default of Bank Rakyat Indonesia is the lowest. In 24, the finding is empirically confirmed. Based on meon model calculation, the probability of default is better than others state bank (7, 4. In fact, in 24, P Bank Rakyat Indonesia bk was announced as he Best National Bank in Indonesia by Bisnis Indonesia award (Moeljono, 26. Second, based on calculation result and reliable information, researcher concludes that financial crisis and volatility of IHSG affect probability of default. 6. Limitations and Recommendations Limitation in this research related to the up to date of data. herefore, the researcher recommends that fuher research should update the period. he next limitation is regarding the effects of financial crisis, volatility of IHSG, and oil price affects probability of default. his causal effect is based on assumption that derived from meon model calculation and other suppoed information such as riwibowo, et al (28. Future studies should investigate this effect empirically does not based on mere assumptions. References rifin, Zainul. nalisis Leverage Operasi dan Leverage Keuangan. Pusat Pengembangan Bahan jar UMB. Baridwan, Zaki. ccounting Intermediate, UGM Black and Scholes, 973, he Pricing of Options and Corporate Liabilities, he University of Chicago Press Black F and M. Scholes.973. he Pricing of Option and Corporate Liabilities, Journal of Political Economy 8, Brigham. F Eugene, Joel F.Houston 24. Fundamentals of Financial Mangement.enth edition.international Student Edition. Crosbie and Bohn,23, Modeling Default Risk Modeling Methodology, Moody s KMV Company S. Munawir, kuntan. nalisa Laporan Keuangan. Edisi keempat Fess Reeve Warren.ccounting, Edition 2.International Student Edition. Hadad, dkk, 24. Probabilitas Kegagalan Korporasi Dengan Menggunakan Model Meon, Bank Indonesia. Hendriksen and Breda,2, ccounting heory, Mc Graw-Hill. New York Hendriksen.S Eldon, Breda van F. Michael. ccounting heory. Fifth Edition.Mc Graw Hill. Hongren, Harison, Bamber. ccounting.6th edition.international edition. Makmun. 22. Efisiensi Kinerja suransi Pemerintah. Kajian Ekonomi and Keuangan Vol 6 No Manurung, 2, Probabilitas Default Perusahaan. Meon, R.973. heory of Rational Option Pricing, Bell Journal of Economics and Management Science 4, 4-73 Meon, R.974. On the Pricing of Corporate Debt: he Risk Structure of Interest Rates, he Journal of Finance 29, Miller and Jordand, 28, Fundamental of Investment, Mc Graw-Hill. New York Reinvensi BUMN Moeljono, Djokosantoso.26. Reinvensi BUMN. Elex Media Komputindo S.-M. Lin, J. nsell, G.. ndreeva, Meon models or credit scoring: modelling default of a small, Credit Research Centre, Management School & Economics he University of Edinburgh Sugiyono.27. Statistika untuk penelitian. Bandung: CV. lfabeta. Sugiyono.28. Metode Penelitian Kuantitatif, Kualitatif and R & D. Bandung: CV.lfabeta. udela and Young (23 dengan judul Meon-model approach to assessing the default risk of UK public companies,publications Group, Bank of England Wajiro, Untoro perry, 25, Warjiyo (25 Default Risk dan Penjaminan Kredit UKM, Buletin Ekonomi Moneter dan Perbankan, Maret 25 9

6 Weygant, Kieso and Kimmel. 25 ccounting Principles. 7H Edition. John Willey & Sons, INC. Wild et al. Financial Statement nalysis, Ninth Edition.Mc Graw Hill. Wild, Subramanyam, and Halsey, 27, Financial Statement nalysis,mc Graw-Hill. New York www. BI.go.id 2